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Penalized Splines and Reproducing Kernel Methods

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  • Pearce, N.D.
  • Wand, M.P.

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  • Pearce, N.D. & Wand, M.P., 2006. "Penalized Splines and Reproducing Kernel Methods," The American Statistician, American Statistical Association, vol. 60, pages 233-240, August.
    • Handle: RePEc:bes:amstat:v:60:y:2006:m:august:p:233-240
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    Cited by:

    1. Wong, Raymond K.W. & Zhang, Xiaoke, 2019. "Nonparametric operator-regularized covariance function estimation for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 131-144.
    2. Shaogao Lv & Xin He & Junhui Wang, 2017. "A unified penalized method for sparse additive quantile models: an RKHS approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 897-923, August.
    3. Anastasia Kostaki & Javier Moguerza & Alberto Olivares & Stelios Psarakis, 2009. "Graduating the age-specific fertility pattern using Support Vector Machines," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(25), pages 599-622.
    4. González, Javier & Muñoz, Alberto, 2010. "Representing functional data in reproducing Kernel Hilbert Spaces with applications to clustering and classification," DES - Working Papers. Statistics and Econometrics. WS ws102713, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Nerini, David & Monestiez, Pascal & Manté, Claude, 2010. "Cokriging for spatial functional data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 409-418, February.

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